
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "163"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 95 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 91 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	24.32%	0.322113	-0.529406	-0.757003	-0.823214	-1.518718	-0.214551	-1.145002	-0.556332	0.8075	0.8046	0.2403	2.951274	2.837139
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	3.23%	-0.428887	-1.122008	-0.950286	-0.824055	-1.550561	-0.189895	-0.863025	-0.427562	0.7516	0.7462	0.2462	2.150378	2.423804
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.277747	-0.828156	-0.379459	-0.807821	-1.500355	-0.649209	-1.877637	0.429918	0.0443	0.0499	0.0093	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-0.614488	-1.288889	-0.928041	-0.236741	-1.288039	-0.067537	-0.887564	0.593092	0.6920	0.6998	0.3610	1.886039	1.784897
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-0.143393	-0.664617	-1.029116	-0.550263	-1.261378	0.564780	0.389933	0.704907	0.6918	0.6985	0.3550	2.049689	1.889654
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	18.28%	-1.083963	-1.197155	-1.206775	-0.745678	-1.282522	-0.232357	-0.949256	-0.709562	0.7358	0.7267	0.2782	1.726347	1.805824
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	0.180812	-0.777451	-1.066546	-1.140635	-1.047736	-0.269817	-0.961793	-0.793679	0.7944	0.7738	0.2212	2.644985	2.535490
2459587	digital_ok	0.00%	-	-	-	2.80%	0.00%	-0.857183	-0.905099	-1.069040	-0.652639	0.630509	0.139235	-1.018493	-0.132790	nan	nan	nan	1.014985	1.048923
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	-0.627341	0.364045	0.320992	1.369400	0.120405	2.566169	-0.076347	1.319006	0.6806	0.6891	0.3531	1.785661	1.715428
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.542178	6.124081	2.209475	3.758299	2.125972	5.066383	0.344647	3.733812	0.0494	0.0497	0.0054	0.000000	0.000000
2459584	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.171055	-0.646913	2.095068	1.261064	0.150433	0.647924	0.415762	1.361311	0.6294	0.6379	0.3603	1.658061	1.425582
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-0.655973	-0.860171	1.434477	0.984753	-0.828676	1.667375	0.725090	2.651103	0.6929	0.7031	0.3727	1.718772	1.845923
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	-0.254779	-0.508141	1.104112	0.947201	-0.543104	0.545944	0.267865	1.981096	0.6928	0.7019	0.3757	1.698375	1.753083
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	0.183244	-0.823115	1.668052	0.589994	-0.259652	0.767350	0.837719	2.844905	0.6993	0.7076	0.3722	1.721679	1.700924
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.266828	-0.607869	1.084870	0.762056	-0.464567	0.667224	3.612051	5.121906	0.6939	0.7019	0.3721	4.852531	4.545226
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.462096	-0.395013	1.706043	1.395655	-0.180205	0.361881	1.069944	1.936321	0.6870	0.6967	0.3748	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.037037	-0.538570	-0.377340	-0.875050	-1.276440	1.168328	-0.148493	1.982073	0.0420	0.0874	0.0100	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	16.22%	-0.304242	-0.954402	1.457360	0.622874	0.503017	0.647586	0.535216	2.629595	0.6886	0.6959	0.3771	1.753660	1.690911
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	0.00%	-0.160229	-0.669338	1.226145	0.804189	0.840700	0.292398	0.125519	1.306918	0.7156	0.7243	0.3701	1.553765	1.563516
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.084190	-0.280747	0.999421	0.603659	0.341890	1.779192	-0.388453	0.019761	0.7114	0.7210	0.3655	1.607234	1.588264
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.697157	-0.595555	1.950939	1.317551	0.343612	2.281537	0.057729	1.869351	0.7099	0.7184	0.3697	1.675150	1.643476
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.120731	-0.848475	0.969709	0.651815	0.054755	0.959584	0.211923	2.167702	0.7135	0.7230	0.3728	1.529646	1.642730
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.929786	-0.503609	-0.557316	-0.999008	-1.858174	0.392001	-0.468842	1.512108	0.0429	0.0833	0.0148	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.515167	0.622067	1.337261	0.953086	1.218421	0.933292	0.741586	1.218611	0.7101	0.7281	0.3568	1.334568	1.192712
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.475256	-0.647061	1.258154	0.868077	1.233586	1.583759	1.136477	0.842800	0.7904	0.7941	0.2215	2.287911	2.003731
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-0.433391	-0.436650	1.691818	1.137746	-0.110689	1.082364	-0.473453	1.378524	0.7026	0.7161	0.3904	1.742293	1.745859
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.042832	0.042832	0.026138	0.026138	-0.845420	-0.845420	-0.928844	-0.928844	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-0.484840	-0.371555	0.092897	-0.245818	2.393667	2.266752	0.273258	1.177712	0.7246	0.7257	0.3847	1.702144	1.631306
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.505827	-0.716322	-0.216666	-0.869925	-1.097495	0.231893	0.763347	2.381827	0.7193	0.7261	0.3761	1.886046	1.869003
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.361050	0.013357	1.047913	0.898618	0.742877	0.564148	0.445407	1.091725	0.7219	0.7310	0.3705	1.867964	1.701811
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.491656	-0.149393	0.912019	0.668156	0.626513	1.210308	0.501137	1.005341	0.7174	0.7299	0.3711	1.810422	1.738460
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.263919	-0.026215	1.080165	0.680663	1.073697	1.048955	0.468632	1.281414	0.7146	0.7245	0.3857	1.774380	1.636288
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.476587	0.056040	0.951762	0.608459	0.784944	0.650893	0.305654	0.912281	0.7162	0.7241	0.3750	1.771739	1.651851
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	13.98%	0.00%	-0.236721	-0.184168	1.341553	1.472424	-0.497217	1.406768	-0.407643	1.492186	0.7207	0.7240	0.3802	1.705818	1.563007
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0419	0.0646	0.0106	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	0.00%	0.557242	-0.224078	1.421318	1.571113	-0.363718	1.289033	-0.082334	2.103016	0.7140	0.7202	0.3793	1.728959	1.553031
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7238	0.7306	0.3847	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7199	0.7259	0.3854	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.242187	-1.055580	2.254312	1.146054	-0.515151	1.294183	-0.440448	1.208742	0.7264	0.7246	0.3864	1.509063	1.559343
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.411773	-0.242630	1.134061	0.730479	0.805404	1.413617	0.243307	0.966300	0.7211	0.7241	0.3818	1.852444	1.685162
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0529	0.0631	0.0059	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	3.24%	-0.065423	-0.782221	1.701550	1.115653	-0.574403	1.186047	0.003389	1.667602	0.7165	0.7182	0.3910	1.947135	1.757238
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.058867	-1.437838	1.087812	-0.782736	0.666546	1.451163	-0.158725	1.952819	0.7655	0.7613	0.3449	1.822752	1.644400
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	-0.545194	-1.121185	1.464870	0.800449	3.335076	0.675609	0.945248	1.315565	0.7568	0.7503	0.3126	1.698428	1.583755
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.45%	-0.258742	0.040531	0.771074	0.697170	-0.209914	0.330851	0.672068	1.802480	0.7686	0.7675	0.3284	1.672958	1.561770
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.839913	-0.308414	3.040827	2.039571	0.416361	1.379056	1.959034	4.765534	0.7499	0.7503	0.4010	4.966293	4.231479
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.196203	-0.066661	0.853764	0.567153	0.001453	-0.431322	0.516147	2.108013	0.8002	0.7905	0.3401	1.859042	1.656180
2459534	digital_ok	100.00%	98.66%	98.66%	0.00%	100.00%	0.00%	4.319018	4.265910	0.030113	-0.140496	2.416978	4.345960	8.982655	8.800974	0.0654	0.0691	0.0128	1.206652	1.209263
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	-0.715139	-0.566014	1.224069	0.673019	-0.319296	0.075663	-0.121689	2.527080	0.7402	0.7422	0.3844	1.745248	1.662438
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	-0.631353	-0.187711	2.006894	1.495765	-0.647905	0.635171	-0.222377	1.452838	0.7451	0.7491	0.3822	1.734215	1.671490
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.368194	-0.152270	0.783171	0.483394	0.581443	1.087372	1.925879	2.644510	0.7621	0.7590	0.3745	1.764131	1.685479
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.028093	0.166957	2.046826	1.125811	2.036438	0.738912	1.460969	3.230952	0.9123	0.9417	0.2878	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	0.487903	-0.758362	2.998083	1.234091	0.865315	3.889638	0.880537	2.390666	nan	nan	nan	5.816629	4.921219
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.232546	-0.327999	2.139067	0.749095	0.068797	-0.257176	1.508460	1.899384	0.7249	0.7131	0.3080	2.048880	1.985128
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.921520	-0.637179	-0.311792	-0.741792	-0.772571	2.529719	1.107442	3.729036	0.0367	0.0488	0.0067	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.399116	1.109817	1.434966	1.094563	0.540190	0.782722	1.301053	1.521850	0.9240	0.9511	0.1539	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.051868	1.104791	1.572664	1.567750	-0.471179	2.556138	1.553538	10.345188	0.0702	0.0873	0.0043	1.345147	1.346998
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.906111	0.454694	2.050948	1.067674	2.844865	2.463335	1.653664	10.518430	0.8835	0.8825	0.4181	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.337601	0.418468	1.153032	0.942291	-0.206428	-0.073187	0.377511	2.430415	0.1456	0.1303	0.0241	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.678010	0.245481	1.015166	0.816055	1.259280	0.754319	-0.372765	1.994606	0.1535	0.1543	0.0246	0.000000	0.000000
2459499	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.292789	0.688929	1.850824	1.295091	1.664617	1.056270	0.586763	1.828256	0.1293	0.1357	0.0115	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.256587	0.174409	1.547235	0.670920	1.594420	0.089809	0.831380	1.182234	0.1346	0.1441	0.0257	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.275658	0.188236	1.258596	0.400214	1.419279	-0.153834	0.387336	0.889859	0.1553	0.1912	0.0269	0.000000	0.000000
2459496	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.725919	0.458724	1.620566	0.798766	1.690542	0.324590	0.034179	0.773327	0.9191	0.9279	0.3547	0.000000	0.000000
2459495	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.302283	0.097650	1.527559	0.624288	1.431553	0.178861	-0.031707	0.614454	0.9159	0.9238	0.3901	0.000000	0.000000
2459494	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	2.052575	1.019529	1.817914	1.288244	1.238016	0.575231	1.845355	1.499636	0.9014	0.9139	0.4044	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	0.066760	-0.543923	1.218563	0.085846	-0.244518	0.528526	1.685516	2.051840	0.7578	0.8054	0.3265	0.359336	0.332666
2459491	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.487179	0.026389	1.749252	0.443402	0.679865	2.305056	-0.376146	6.176857	0.7977	0.8203	0.2589	3.246019	3.397233
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	0.105004	-0.401333	1.336074	-0.536809	-0.018011	1.442858	-0.300434	1.672764	0.7508	0.8057	0.3664	1.977740	1.783215
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	-0.319086	-0.684692	1.499205	0.072077	-0.007795	0.437564	0.341333	2.312957	0.7696	0.8131	0.3206	1.853495	1.677193
2459488	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.093623	-0.760593	1.359229	0.433212	-0.083489	2.927830	0.647980	4.706882	0.7712	0.8153	0.3462	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 163 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 163, Node 14:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 163: 2459619

Antenna 163: 2459618

Antenna 163: 2459617

Antenna 163: 2459616

Antenna 163: 2459615

Antenna 163: 2459614

Antenna 163: 2459613

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Antenna 163: 2459609

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Antenna 163: 2459589

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Antenna 163: 2459566

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Antenna 163: 2459552

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Antenna 163: 2459542

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Antenna 163: 2459536

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Antenna 163: 2459532

Antenna 163: 2459530

Antenna 163: 2459527

Antenna 163: 2459524

Antenna 163: 2459523

Antenna 163: 2459522

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶